Precise Segmentation of 3-D Magnetic Resonance Angiography

El-Baz, Ayman; Elnakib, Ahmed; Khalifa, Fahmi; El-Ghar, Mohamed Abou; McClure, Patrick; Soliman, Ahmed; Gimelrfarb, G.

doi:10.1109/tbme.2012.2196434

Cited by 104 publications

(84 citation statements)

References 27 publications

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“…To separate p ob and p bg , the mixed empirical distribution of all the pixel intensities is approximated with a linear combination of discrete Gaussians (LCDG) 1 [101][102][103].…”

Section: Conditional Intensity Model For Ce-cmr Slicementioning

confidence: 99%

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Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Elnakib¹

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DEDICATIONThis dissertation is dedicated to my mother, my father, and my wife for their love, patience, and support during the completion of this endeavor.iii ACKNOWLEDGMENTSIn the Name of Allah, the most beneficent, the most merciful. All deepest thanks are due to Allah, the compassionate for the uncountable gifts given to me.I would like to express my sincere gratitude to Professor El-Baz, my dissertation advisor, for the immeasurable amount of support and guidance he has provided throughout this study. He gave me the opportunity to work in his distinguished research group and provided me with an exciting working environment that facilitate many opportunities to develop new ideas and working on promising applications. Dr. El-Baz is always willing to give, share, and appreciate. I knew that I could always expect full credit for my accomplishments. He always supported me with novel ideas that helped me to publish my works in the top international conference and journal papers. It is worthy to mention that Professor El-Baz is a great advisor as well as a good friend who helps any Ph.D. student who works with him in high energy level. I cannot thank him enough, and shall remain grateful for all he has done. Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality.This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in vi the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure-the largest fiber bundle that connects the two hemispheres in the brain-for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object's visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching...

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“…To separate p ob and p bg , the mixed empirical distribution of all the pixel intensities is approximated with a linear combination of discrete Gaussians (LCDG) 1 [101][102][103].…”

Section: Conditional Intensity Model For Ce-cmr Slicementioning

confidence: 99%

“…, c α , respectively. The LCDG of Equation (5), including the numbers C p and C n of its components, is identified using the expectation-maximization (EM)-based algorithm introduced in [100][101][102][103][107][108][109][110][111][112].…”

Section: Conditional Intensity Model For Ce-cmr Slicementioning

confidence: 99%

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Elnakib¹

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“…// Convolve the generated kernel with the input volume g. with the classical Expectation-Maximization (EM) algorithm and its modification accounting for the alternate signs of the DGs [375]. Then the obtained LCDG is separated into conditional lung and chest intensity models for defining the conditional IRF of image signals, given a region map:…”

Section: Algorithm 3 Gaussian Scale Space Smoothingmentioning

confidence: 99%

“…• Extending the presented segmentation technique to deal with segmenting different structures (multi-class labeling) of the brain [375,. This process will be of a great importance to extract and quantify the brain structures (e.g., white matter, gray matter, CSF, etc) to be used in different CAD systems for the brain.…”

Section: Chapter VI Conclusion and Future Workmentioning

confidence: 99%

Computational methods for the analysis of functional 4D-CT chest images.

Soliman¹

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DEDICATIONThis dissertation is dedicated to my father's soul.iii ACKNOWLEDGMENTS In the name of Allah the most merciful, the most compassionate. All deepest thanks are due to Almighty Allah for the uncountable gifts given to me. Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients.v The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decisionmaking. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification.This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today's clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered...

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“…For example, Ghose et al [181] proposed a probabilistic graph-cut-based framework for 3D T2-MRI prostate segmentation based on a probabilistic atlas. Firjany et al [147] proposed a Markov random field (MRF) image model [182][183][184][185][186][187][188][189][190][191][192][193][194][195][196] for 2D DCE-MRI prostate segmentation that combined a graph-cut approach with a prior shape model of the prostate and the visual appearance of the prostate image, modeled using a linear combination of discrete Gaussian (LCDG) [197][198][199][200][201][202][203][204][205][206][207][208] Their method was later extended in [209,210] to allow for 3D prostate segmentation from DCE-MRI volumes. The main limitation of graph-cut techniques is that they are prone to minimizing the size of the segmented region [211].…”

Section: Mri-based Cad Systemsmentioning

confidence: 99%

A novel NMF-based DWI CAD framework for prostate cancer.

McClure¹

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Precise Segmentation of 3-D Magnetic Resonance Angiography

Cited by 104 publications

References 27 publications

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Computational methods for the analysis of functional 4D-CT chest images.

A novel NMF-based DWI CAD framework for prostate cancer.

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